Since Swin-Transformer achieves state-of-the-art under supervised training, it is adopted as the backbone to validate the transfer ability of our method in the task of object detection and instance segmentation. We perform object detection experiments with MS COCO [1]} dataset and Mask R-CNN detector [2]} framework. MS COCO is a popular benchmark for object detection, with 118K images in training set and 5K images for validation. This dataset contains annotations for 81 classes. Box AP and mask AP are reported on the validation set. As to training settings, we follow the default 1x schedule with 12 epochs. The shorter edges of the input images are resized to be 800 and the longer edges are limited by 1333 pixels. AdamW optimizer is used, and all hyper-parameters follow the original paper.
